DataRobot-Research/drdocs huggingface dataset:
# Version 7.1.3 {: #version-713 }

_August 27, 2021_

The DataRobot v7.1.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:

* [Features introduced in v7.1.0](v7.1.0-aml)

## Issues fixed in v7.1.3 {: #issues-fixed-in-v713 }

The following issues have been fixed since Enterprise release v7.1.2:

### Enterprise {: #enterprise }

* EP-1535: Fixes an issue with the map tile management workflow when Minio is used as storage on Docker-based installations.
* EP-1495: Sets `PYSPARK_PYTHON` and `PYSPARK_DRIVER_PYTHON` in datarobot-scoring to point to DataRobot's python.

### Platform {: #platform }

* PLT-3318: Fixes an issue where a user with read-only access to deployments through RBAC was not able to view deployments shared with them as a _User_.

### MLOps {: #mlops }

* RAPTOR-5955: Fixes an issue where models did not retain the content of a previous version when changing the base environment in the Custom Model Workshop.
* RAPTOR-6069: Fixes an issue causing the custom models GitHub integration to fail with SSL handshake error on verification of self-signed certificates. To fix SSL handshake, configure DataRobot to allow self-signed SSL certificates: set `ALLOW_SELF_SIGNED_CERTS: True` in the config.yaml under `app_configuration.drenv_override` and `re-run bin/datarobot install`.
* PRED-6240: Fixes an issue where drift tracking was not correctly set from the job-definitions UI (was previously `FALSE` by default).

### Business Operations {: #business-operations }

* BOPS-1289: Enable support for `ALLOW_SELF_SIGNED_CERTS` in the AI App Builder.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Get a prediction server ID
dataset_name: N/A
description: Learn how to retrieve a prediction server ID using cURL commands from the REST API or by using the DataRobot Python client.
domain: DSX
expiration_date: 6-20-2022
owner: nathan.goudreault@datarobot.com
url: docs.datarobot.com/en/tutorials/using-the-api/pred-server-id.html
---

# Get a prediction server ID {: #get-a-prediction-server-id }

In order to make predictions from a deployment via DataRobot's [Prediction API](dr-predapi), you need a prediction server ID. In this tutorial, you'll learn how to retrieve the ID using cURL commands from the REST API or by using the DataRobot Python client. Once obtained, you can use the prediction server ID to deploy a model and make predictions.

!!! note
    Before proceeding, note that an API key is required for this tutorial. Reference the [Create API keys](api-key-mgmt) tutorial for more information.

=== "cURL"

    ``` shell
    curl -v \
    -H "Authorization: Bearer API_KEY" \ YOUR_DR_URL/api/v2/predictionServers/

    Example
    API_KEY=YOUR_API_KEY
    ENDPOINT=YOUR_DR_URL/api/v2/predictionServers/

    curl -v \
    -H "Authorization: Bearer $API_KEY" \
    $ENDPOINT
    ```

=== "Python"

    Before continuing with Python, be sure you have installed the DataRobot Python client and configured your connection to DataRobot as outlined in the [API quickstart guide](api-quickstart/index).

    ``` python
    # Set up your environment
    import os
    import datarobot as dr

    API_KEY = os.environ["API_KEY"]
    YOUR_DR_URL = os.environ["YOUR_DR_URL"]
    FILE_PATH = os.environ["FILE_PATH"]
    ENDPOINT = YOUR_DR_URL+"/api/v2"

    # Instantiate DataRobot instance
    dr.Client(
        token=API_KEY,
        endpoint=ENDPOINT
    )

    prediction_server_id = dr.PredictionServer.list()[0].id
    print(prediction_server_id)
    ```


## Documentation {: #documentation }

The following provides additional documentation for features mentioned in this tutorial.

* [API key management](api-key-mgmt#api-key-management)
* [DataRobot Developers portal](https://developers.datarobot.com/){ target=_blank }
* [DataRobot Prediction API](dr-predapi)
---
title: Companion tools
description: This section includes links to user documentation for DataRobot companion tools, including Algorithmia and Data Prep (Paxata).
---

# Companion tools {: #companion-tools }

The information in this section provides user documentation for Data Prep and Notebooks.

Topic | Describes...
----- | ------
Paxata Data Prep | Use the Paxata Data Prep application to visually explore, profile, clean, enrich, and shape diverse data into AI assets.<br>[Download PDF](pdf/Paxata-Data-Prep-EN-v2021.2.pdf){:target="_blank"}
[Algorithmia](algorithmia) | Use Algorithmia, an MLOps platform, to deploy, govern, and monitor your models as microservices.
---
title: Import data
description: DataRobot lets you import data using multiple methods, including uploading dataset files locally, uploading from URLs, and connecting to data sources.

---

# Import data {: #import-data }

Data can be ingested into DataRobot from your local system, a URL, and through data connections to common databases and data lakes. A critical part of the data ingestion process is [Exploratory Data Analysis (EDA)](eda-explained). EDA happens twice within DataRobot, once when data is ingested and again once a target has been selected and modeling has begun.

You can import data directly into the DataRobot platform or you can import into the AI Catalog, a centralized collaboration hub for working with data and related assets. The catalog allows you to seamlessly find, understand, share, tag, and reuse data. The following sections provide guidelines and steps for importing data.

Topic | Describes...
----- | ------
[Import directly into DataRobot](import-to-dr) | In DataRobot, you can import a dataset file, import from a URL, import from AWS S3, among other methods.
[Import into the AI Catalog](catalog) | Import data into the AI Catalog and from there, create a DataRobot project. In the catalog, you can transform the data using SQL, and create and schedule snapshots of your data.
[Import large datasets](large-data/index) | Methods of working with large datasets (greater than 10GB).
---
title: Examples directory
description: Use sample code available in the MLOps agent tarball as a starting point for creating and managing deployments. Examples include model configuration, data, and scripts used to create deployments and run the examples.

---

# Examples directory {: #examples-directory }

The `examples` directory in the MLOps agent tarball contains both sample code (snippets for manual inspection) and example code (self-contained examples that you can run) in Python and Java. Navigate to the subdirectory for the language you wish to use and reference the respective `README` for further instruction.

The examples directory includes model configuration, data, and scripts used to create deployments and run the examples, using Python to create the model package and deployment programmatically. Therefore, you must install the Python version of the MLOps library (described below). These examples also use the [MLOps Command Line Interface (mlops-cli)](agent-use#monitor-using-the-mlops-cli) to set up deployments and perform deployment actions. You must provide the `MLOPS_SERVICE_URL` and `MLOPS_API_TOKEN` environment variables to use the `mlops-cli`. In addition, most examples use the `mlops-cli` to upload monitoring data for faster setup; however, while the `mlops-cli` tool is suitable for simple use cases, you should use the agent for production scenarios.

## Run code examples with Python {: #run-code-examples-with-python }

To run the Python code examples, you must install the dependencies used by the examples:

``` sh
pip install -r examples/python/requirements.txt
```

See the `README` file in each example directory for further example-specific configuration requirements. In general, to run an example:

1. Initialize the model package and deployment:

     ``` sh
     ./create_deployment.sh
     ```

2. Generate predictions and report statistics to DataRobot:

     ``` sh
     ./run_example.sh
     ```

3. Verify that metrics were sent successfully:

     ``` sh
     ./verify_example.sh
     ```

4. Delete resources created in the example:

     ``` sh
     ./cleanup.sh
     ```
---
title: Make predictions
description: After you create an experiment and train models, you can upload scoring data, make predictions, and download the results.

---

# Make predictions {: #make-predictions }

After you create an [experiment](wb-experiment/index) and train models, you can make predictions to validate those models.

To make predictions with a model in a Workbench experiment:

1. Select the model from the **Models** list and then click **Model actions > Make predictions**.

	![](images/wb-model-action-pred.png)

2. On the **Make Predictions** page, upload a **Prediction source**:

	* Click, drag, and drop a file into the **Prediction source** group box.

	* Click **Choose file** to upload a **Local file** or a dataset stored in the **AI Catalog**.

	![](images/wb-pred-source.png)

	!!! note
		When you upload a prediction dataset, it is automatically stored in the **AI Catalog** once the upload is complete. Be sure not to navigate away from the page during the upload, or the dataset will not be stored in the catalog. If the dataset is still processing after the upload, that means DataRobot is [running EDA](eda-explained){ target=_blank } on the dataset before it becomes available for use.

3. In the **Prediction options** section, to write input features (columns) to the prediction results file alongside the predictions, enable **Include input features**:

	* To filter for and include specific features from the dataset, select **Specific features** and enter one or more feature names to add them.

	* To include every feature from the dataset, select **All features**.

	![](images/wb-pred-options.png)

	!!! note
		You can only append a feature (column) present in the original dataset, although the feature does not have to have been part of the feature list used to build the model. Derived features are not included.

4. After you configure the **Prediction options**, click **Compute and download predictions** to start scoring the data.

5. When scoring completes, under **Your recent predictions**:

	* If scoring is successful, click the download icon (![](images/icon-download-pred.png)) to download a predictions file.

		![](images/wb-pred-download.png)

	* If the prediction job fails, click **View logs** to view and optionally copy the run details.

	!!! note
		Predictions are available for download for the next 48 hours.
# Version 7.0.3 {: #version-703 }

_December 21, 2021_

The DataRobot v7.0.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:

* [Features introduced in v7.0.0](v7.0.0-aml)

## Issues fixed in v7.0.3 {: #issues-fixed-in-v703 }

The following issues have been fixed since Enterprise release v7.0.2:

### Enterprise {: #enterprise }

* EP-1062: Fixes a Python 3 incompatibility in the Boto library issue when connecting to AWS Cloudwatch behind an HTTPS proxy.
* EP-1345: Fixes an issue that caused a `KeyError` during DB migration.
* EP-2133: Updates Scoring Code build plugins to avoid the inclusion of vulnerable log4j versions.

### Platform {: #platform }

* PLT-3709: Prevents the `ENABLE_DATA_ENGINE_ON_K8S` feature flag from appearing under Developer UI feature flags for on-prem.
* PLT-3812: Fixes an issue where delegation would not turn off.

### Custom Models {: #custom-models}

* RAPTOR-5240: Fixes an issue where custom model dependency management failed to parse Python package names containing a period (`.`).
* RAPTOR-5955: Fixes an issue in the Custom Model Workshop that created a new minor version with empty model content when changing the base environment. Now it properly retains the content of a previous version.
* RAPTOR-6069: Fixes an issue causing the custom models GitHub integration to fail with a SSL handshake error on verification of self-signed certificates.

### Trust & Explainability {: #trust-explainability }

* TED-2688: Fixes issue where the **Bias vs Accuracy** page would not load when using a fairness metric that's independent of the model's prediction threshold.
* XAI-4106: Fixes the format of the `shapBaseValue` field in  v2 Predictions Public API response to be a scalar float value which corresponds with other APIs and documentation.
* XAI-4238: Fixes an issue when loading certain models with SHAP explainers that were built in DataRobot version 5.2.2, which failed to load after upgrading to version 6.3.

### Time Series {: #time-series }

* TIME-8745: `COMPUTE_TIMESERIES_AOT_THRESHOLD` can now be configured from an environment.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Model logs for model packages
description: View the model logs for a model package to see a history of the successful operations (INFO status) and errors (ERROR status).
section_name: MLOps
maturity: public-preview
---

# Model logs for model packages {: #model-logs-for-model-packages }

!!! info "Availability information"
    Model logs for model packages in the Model Registry are off by default. Contact your DataRobot representative or administrator for information on enabling this feature.

    <b>Feature flag:</b> Enable Model Logs for Model Packages

A model package's model logs display information about the operations of the underlying model. This information can help you identify and fix errors. For example, compliance documentation requires DataRobot to execute many jobs, some of which run sequentially and some in parallel. These jobs may fail, and reading the logs can help you identify the cause of the failure (e.g., the Feature Effects job fails because a model does not handle null values).

!!! important
    In the Model Registry, a model package's **Model Logs** tab _only_ reports the operations of the underlying model, not the model package operations (e.g., model package deployment time).

To view the model logs for a model package:

1. In the **Model Registry**, click **Model Packages**.

2. Click a model package in the list, and then click **Model Logs**.

3. On the **Model Logs** tab, review the timestamped log entries:

    ![](images/pp-model-pkg-logs.png)

    | | Information | Description |
    |-|-------------|-------------|
    | ![](images/icon-1.png) | Date / Time | The date and time the model log event was recorded. |
    | ![](images/icon-2.png) | Status      | The status the log entry reports: <ul><li><span style="color:#3BC169">INFO</span>: Reports a successful operation.</li><li><span style="color:#E74D4D">ERROR</span>: Reports an unsuccessful operation.</li></ul> |
    | ![](images/icon-3.png) | Message     | The description of the successful operation (INFO), or the reason for the failed operation (ERROR). This information can help you troubleshoot the root cause of the error.  |

4. If you can't locate the log entry for the error you need to fix, it may be an older log entry not shown in the current view. Click **Load older logs** to expand the **Model Logs** view.

    ![](images/pp-model-pkg-logs-load.png)

    !!! tip
        Look for the older log entries at the top of the **Model Logs**; they are added to the top of the existing log history.
# Version 8.0.1 {: #version-801 }

_March 30, 2022_

The DataRobot v8.0.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

## Issues fixed in v8.0.1 {: #issues-fixed-in-v801 }

The following issues have been fixed since Enterprise release v8.0.0:

### Time series {: #time-series }

* TIME-9875: Fixes an issue causing the feature over time calculation to fail when the feature is categorical and has a unique value.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
# Amazon Athena {: #amazon-athena }

## Supported authentication {: #supported-authentication }

- AWS Credential

## Prerequisites {: #prerequisites }

The following is required before connecting to Amazon Athena in DataRobot:

- [AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html){ target=_blank }
    - Username = AWS access key
    - Password = AWS secret access key

- Athena managed policies attached to the AWS account


## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Amazon Athena:

Required field | Description | Documentation
--------------- | ----------  | -----------
`address`       | The service endpoint used to connect to AWS.<br><br>**Example:**<br> `athena.us-east-1.amazonaws.com:448`  | [AWS documentation](https://docs.aws.amazon.com/general/latest/gr/athena.html){ target=_blank }
`AwsRegion`       | Separate geographic areas that AWS uses to house its infrastructure.<br><br>**Example:**<br> `us-east-1`  |   [AWS documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html){ target=_blank }
`S3OutputLocation`       | Specifies the path to your data in Amazon S3.  |   [AWS documentation](https://docs.aws.amazon.com/athena/latest/ug/tables-location-format.html){ target=_blank }

## Caveats {: #caveats }

- Due to a limitation with the JDBC driver itself, the **Existing Table** tab is not available for Athena data connections. You must select **SQL Query** and enter a SQL query to retrieve data from Athena.

{% include 'includes/data-conn-trouble.md' %}
---
title: Monitor AWS Sagemaker models with MLOps
description: Train and host a SageMaker model that can be monitored in the DataRobot platform.

---

# Monitor AWS Sagemaker models with MLOps {: #monitor-aws-sagemaker-models-with-mlops }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/tree/main/end-to-end/monitor_sagemaker_model_in_DataRobot){ .md-button }

DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production.

You can deploy models to the production environment of your choice and continuously monitor the health and accuracy of your models, among other metrics.

AWS Sagemaker is a fully managed service that allows data scientists and developers to build, train, and deploy machine learning models. DataRobot MLOps with its AWS Sagemaker integration provides an end-to-end solution for managing machine learning models at scale, you can easily monitor the performance of your machine learning models in real-time, and quickly identify and resolve any issues that arise.
---
title: Deployment settings
description:

---

# Deployment settings

After you [create and configure a deployment](add-deploy-info), you can use the settings tabs for individual features to add or update deployment functionality: 

 Topic | Describes
-------|------------
[Set up service health monitoring](service-health-settings) | Enable [segmented analysis](deploy-segment) to assess service health, data drift, and accuracy statistics by filtering them into unique segment attributes and values. 
[Set up data drift monitoring](data-drift-settings)         | Enable [data drift monitoring](data-drift) on a deployment's Data Drift Settings tab. 
[Set up accuracy monitoring](accuracy-settings)             | Enable [accuracy monitoring](deploy-accuracy) on a deployment's Accuracy Settings tab.
[Set up fairness monitoring](fairness-settings)             | Enable [fairness monitoring](mlops-fairness) on a deployment's Fairness Settings tab. 
[Set up humility rules](humility-settings)                  | Enable [humility monitoring](humble) by creating rules which enable models to recognize, in real-time, when they make uncertain predictions or receive data they have not seen before.
[Configure retraining](retraining-settings)                 | Enable [Automated Retraining](set-up-auto-retraining) for a deployment by defining the general retraining settings and then creating retraining policies.
[Configure challengers](challengers-settings)               | Enable [challenger comparison](challengers) by configuring a deployment to store prediction request data at the row level and replay predictions on a schedule.
[Review predictions settings](predictions-settings)         | Review the Predictions Settings tab to view details about your deployment's inference data.
[Enable data export](data-export-settings)                  | Enable [data export](data-export) to compute and monitor custom business or performance metrics. 
[Set prediction intervals for time series deployments](predictions-settings#set-prediction-intervals-for-time-series-deployments) | Enable [prediction intervals](ts-predictions#prediction-preview) in the prediction response for deployed time series models.
---
title: Series Insights (clustering)
description: Available for clustering projects, the Series Insights tab provides series clustering information in both charted and tabular format.

---

# Series Insights (clustering) {: #series-insights-clustering }

The **Series Insights** tab for time series [clustering](ts-clustering) provides series clustering information, including the cluster to which the series belongs, as well as series row and date information. Histograms, a non-clustering version of [**Series Insights**](series-insights-multi) for multiseries projects, are also available. The insight is helpful in identifying which cluster any given series was assigned to, and also provides an at-a-glance check that no single series is inappropriately dominant. 

For multiseries, insights are reported in both charted and tabular format:

*  The [histogram](#series-insights-histogram) for each cluster, which includes the number of series, the number of total rows, and the percentage of the dataset that belongs to that cluster.
*  The [table](#series-insights-table-view) displays basic information for each series, such as row count, dates, and cluster membership.

![](images/cl-series-accuracy.png)

Note that for large datasets, DataRobot computes scores and values after downsampling.

## Use Series Insights {: #use-series-insights }

On first opening, the **Series Insight** chart displays a list of the series, sorted alphanumerically by series ID.

![](images/cluster-insights-1.png)

Click **Compute cluster scores** to run histogram calculations and populate the table _for the current model_:

![](images/cluster-insights-2.png)

###  Histogram {: #histogram }

The histogram provides an at-a-glance indication of the make up of each cluster.

![](images/cluster-insights-3.png)

The following table describes the tools for working with the histogram:

| | Element | Description |
|---|---|---|
| ![](images/icon-1.png) | Plot | Bins clusters by number of rows, percentage of total rows, or number of series in the cluster. To see all values, hover on a bin. |
| ![](images/icon-2.png) | Sort by | Sets the sorting criteria for the bins. |
| ![](images/icon-3.png) | Table filter | Filters the bins represented in the table below the histogram. |

Hovering on a bin displays the values for each of the plot criteria:

![](images/cluster-insights-4.png)

###  Table view {: #table-view }

The table below the histogram provides cluster-specific information for all series in the project dataset or, if filters are applied from the histogram, for series related to the selected clusters.

![](images/cluster-insights-5.png)

To work with the table:

* Use the search function to view metrics for any series.
* Use **Options** (![](images/icon-gear.png)) to set whether the series length information is reported as a time step or number of rows. Additionally, you can download the table data.
* Note that the start and end dates display the first and last timestamps of the series in the dataset.